AI vs npm Supply Chain Attacks: Stop Shai-Hulud Fast

A single malicious npm dependency can execute on every developer laptop and CI runner that touches it. That’s not theory—it’s exactly why the Shai-Hulud worm (and its wider November resurgence, often called Shai-Hulud 2.0) is such an uncomfortable story for teams building in JavaScript.

The numbers alone explain the urgency: the renewed campaign reached tens of thousands of GitHub repositories, including over 25,000 malicious repositories across ~350 users. The attacker’s trick wasn’t subtle. It was effective: run earlier (at pre-install), steal more secrets, spread faster, and if it can’t steal—try to wipe a user’s home directory.

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: you won’t “process” your way out of supply chain risk at npm scale. You need automation that understands behavior across dependencies, build pipelines, and identities. AI isn’t magic, but it’s the most practical way to spot dependency attacks early—before they turn into a week of credential rotation, CI downtime, and incident response.

What Shai-Hulud changes about npm supply chain risk

Shai-Hulud is a reminder that modern supply chain attacks don’t need to break your perimeter; they just need to become your build.

At a high level, the worm’s playbook is straightforward:

• Initial access likely starts with credential harvesting (phishing that spoofs npm prompts, including MFA “updates”).
• A malicious package runs an install script that searches for secrets such as .npmrc tokens, GitHub personal access tokens, SSH keys, and cloud credentials.
• Stolen secrets are exfiltrated—not just to a hidden server, but often into public GitHub repositories created under the victim’s account.
• Then comes the scaling move: it uses the victim’s npm token to publish compromised versions of other packages the victim maintains.

The escalation in “Shai-Hulud 2.0”: pre-install + sabotage

The November campaign widened the blast radius by shifting execution to pre-install. That’s a big deal because it:

• Removes human friction: it runs on build servers and developer machines automatically.
• Slips past late-stage checks: many controls focus on scanning code after install, during build or packaging.

Even worse, the updated campaign added an aggressive fallback: if it can’t steal or exfiltrate, it attempts to securely overwrite and delete writable files in the user’s home directory. That’s a pivot from “steal secrets” to “break engineering.” It’s disruptive enough to become a denial-of-service against your CI/CD capability.

And it disguises itself. New payload names like setup_bun.js and bun_environment.js make it look like a helpful Bun installer. The core payload can be huge (10MB+) and heavily obfuscated, with execution delayed via a detached background process so the install looks normal.

Snippet-worthy reality: When malware runs in pre-install, your CI system becomes the delivery mechanism.

Why JavaScript ecosystems are so attractive to worms

Attackers like npm for the same reason developers do: it’s fast, composable, and interconnected.

A typical modern JavaScript app can pull in hundreds to thousands of transitive dependencies. That creates three structural advantages for attackers:

1. Distribution is built-in: one compromised maintainer account can publish to packages downstream teams already trust.
2. Execution is expected: install scripts are common, and many teams don’t treat them as “code execution at the perimeter.”
3. Secrets are nearby: developer machines and CI runners are rich with tokens, environment variables, and cached credentials.

This is where AI in cybersecurity becomes practical: the problem is too large for manual review, and too dynamic for static allowlists.

Where AI actually helps: catching dependency attacks earlier

AI doesn’t “solve” supply chain security. It improves your odds by detecting patterns humans miss and by correlating weak signals across tooling that rarely talks to each other.

Here are four concrete places AI-based detection and automation pay off against threats like Shai-Hulud.

1) AI-driven dependency risk scoring (beyond CVEs)

Traditional dependency scanning is great at known vulnerabilities, but supply chain attacks often arrive before a CVE exists—or never get one.

AI can flag suspicious packages using behavioral and ecosystem signals, such as:

• Maintainer behavior changes (sudden burst of releases, unusual publish times)
• Package anomalies (unexpected install scripts, sudden size jumps, unusual obfuscation density)
• Repository health signals (new accounts publishing many packages, copied README patterns, repeated templates)
• Dependency graph “oddities” (a color library suddenly pulling network libraries, process utilities, or credential tooling)

This kind of risk model is especially useful when attackers deliberately bypass “known bad” lists.

2) Install-time behavior analytics (the gap Shai-Hulud targets)

Shai-Hulud’s biggest advantage is when it runs. So your best counter is to watch what happens at that moment.

AI-assisted runtime analytics can detect suspicious install-time behaviors across developer endpoints and CI runners:

• Install scripts spawning shells unexpectedly (bash, sh, zsh)
• Attempts to enumerate secrets (.npmrc, SSH directories, environment scraping)
• Network calls during install to unusual domains or one-off endpoints
• Creation/modification of workflow files and CI runner registration behavior

If you’re only scanning source code, you’re late. If you’re monitoring behavior during install, you’re early.

3) Identity + repo telemetry correlation (GitHub as an exfil channel)

One of the nastier elements described in the campaign: stolen secrets being pushed to public GitHub repos created under victim accounts, with recognizable descriptions.

AI is strong at correlating identity events across systems:

• A developer’s GitHub account creates a new public repo
• Minutes later, an npm publish happens from the same identity
• A CI runner registers as self-hosted unexpectedly
• A workflow like discussion.yaml appears without a matching PR or ticket

Individually these events might not page anyone. Together, they’re a story.

4) SOC automation: shrink time-to-containment

For lead security teams, the hardest part of supply chain incidents isn’t detecting one bad package. It’s executing containment across:

• dozens of repos
• multiple CI systems
• developer laptops
• secret stores
• cloud environments

AI helps by automating the repetitive parts reliably:

• Identify where compromised versions appear across lockfiles
• Open tickets/PRs to pin safe versions
• Trigger credential rotation playbooks based on which secrets were present
• Quarantine runners/endpoints that exhibit install-time exfil patterns

This is the part that turns “we detected it” into “we stopped it.”

A pragmatic defense plan for npm supply chain attacks

Most companies get this wrong by focusing on one control. You need layers that match the attacker’s path: dependency → install → secrets → identity → publish → spread.

Immediate actions (if you suspect exposure)

If you think a Shai-Hulud-like event touched your environment, act as if any developer-accessible secret is compromised.

1. Rotate credentials

   • npm access tokens
   • GitHub PATs
   • SSH keys
   • cloud API keys (AWS/GCP/Azure) and third-party tokens

2. Audit lockfiles, not just package.json

   • Review package-lock.json, yarn.lock, pnpm-lock.yaml
   • Pin known-good versions and remove suspicious packages

3. Review GitHub account activity

   • Unrecognized public repos
   • Unexpected commits
   • New/modified GitHub Actions workflows
   • Self-hosted runner registrations

4. Enforce strong MFA and tighten token policies

   • Prefer short-lived tokens where possible
   • Scope tokens aggressively (least privilege)

Hardening actions that prevent the “worm effect”

The goal is to stop automated propagation by removing easy places to steal and reuse credentials.

• Block outbound network access during dependency install in CI where feasible (or allowlist known registries)
• Disallow install scripts unless explicitly approved (or run installs in sandboxed containers)
• Require lockfiles and integrity checks across builds
• Separate build identities from human identities (CI should use dedicated service accounts)
• Reduce secret sprawl by moving tokens out of developer machines and into managed secret stores

Where to apply AI controls (so it’s not “AI theater”)

If you’re investing in AI in cybersecurity, put it where it measurably reduces risk:

• Anomaly detection on dependency updates: flag risky releases before they merge
• Endpoint and CI behavior analytics: detect pre-install exfiltration patterns
• Identity analytics: catch repo creation/publish patterns that don’t match normal workflows
• Automated containment: fast rollback, token revocation, runner quarantine

A simple success metric I like: Could we detect and contain a malicious pre-install script within one build cycle? If the answer is no, your controls are mostly paperwork.

People also ask: “Can AI stop the next npm supply chain attack?”

Yes—if you treat it as detection plus response, not prediction.

AI won’t reliably predict which package becomes malicious next month. What it can do, very well, is:

• spot abnormal install-time behavior
• catch identity misuse patterns (token abuse, repo exfil)
• prioritize which dependency changes are genuinely suspicious
• automate the containment steps humans are too slow to execute at scale

That combination is how you stop the “worm” part—the exponential spread.

What this means for 2026 planning (and why it’s a board-level risk)

Supply chain attacks like Shai-Hulud are trending toward two outcomes: faster execution and higher impact. The November campaign’s sabotage behavior is the tell. Attackers aren’t just stealing; they’re aiming to cripple engineering throughput.

For security leaders heading into 2026 budgets, this is a clean argument for AI-assisted security operations:

• You can’t manually review dependency graphs at modern scale.
• You can’t rely on CVEs to show up on time.
• You can’t accept install-time code execution without runtime visibility.

If you’re building an AI in cybersecurity roadmap, make software supply chain visibility a first-class use case, right alongside phishing and endpoint detection.

Most teams won’t be taken down by a “mystery zero-day.” They’ll be taken down by a dependency update that looked routine.

If you had to bet: would your current pipeline notice a pre-install script exfiltrating tokens and creating a public repo under a developer’s account—before production deploys?